Unlocking knowledge-sharing live streaming e-commerce: An LLM-empowered analytics framework for book sales prediction

Streamers’ discourse plays a key role in shaping purchasing decisions in live streaming e-commerce, especially in knowledge-sharing formats where product promotion is combined with information delivery. Previous studies have shown that streamers’ discourse can influence product sales, with few studies systematically extracting semantic features across different dimensions and quantifying their impact on sales prediction performance. The main contribution of our research is the design of a predictive framework for sales in knowledge-sharing live streaming. The framework integrates social support theory with fine-tuned large language models (LLMs) to systematically extract multi-dimensional semantic cues from streamers’ discourse for sales prediction. We collected data from 80 live streams across 35 Douyin rooms over two months for our experiments. In the social support classification experiment, the fine-tuned Ernie-SFT model outperformed the best baseline LLM, with improvements of 11.12% in accuracy, 11.87% in weighted F1-score, and 7.83% in macro F1-score. In the sales prediction experiments, we validated the proposed framework using four mainstream classifiers and observed consistent performance gains. The best-performing classifier achieved improvements of 12.53% in accuracy, 10.83% in weighted F1-score, and 4.24% in macro F1-score. These findings highlight the strong predictive value of social support features embedded in streamers’ discourse, offering actionable insights for streamers and enabling data-driven optimization strategies for platforms.